REVIEW OpenAccess Surveyofradioresourcemanagement

Song et al. EURASIP Journal onWireless Communications andNetworking (2016) 2016:140 DOI 10.1186/s13638-016-0636-y

REVIEW Open Access

Survey of radio resource managementissues and proposals for energy-efficientcellular networks that will cover billions ofmachinesQipeng Song1,2*, Loutfi Nuaymi1,2 and Xavier Lagrange1,2

Abstract

A huge increase of machines attached to wireless networks is expected in the next few years. A large part of thesemachines will be covered by some wireless wide area networks. The arrival of cellular M2M (machine-to-machine)communication poses new requirements due to its specific characteristics. For most of the cellular M2M applications,the essential requirement is low energy consumption level or high energy efficiency. This survey provides a globalview of the network technologies previewed for cellular M2M. In this survey, we study the existing classifications ofM2M applications according to different criteria in the literature. The comparison of traffic characteristics betweenM2M and human-to-human is also proposed. Quality of service (QoS) requirements for typical M2M applications areresumed. The advance of reference M2M network architectures proposed by the Standard Development Organization(SDO) is investigated. We identify two possible effort directions to improve the energy efficiency for cellular M2M. Thefirst one is to evolve the current existing 3rd Generation Partnership Project (3GPP) Consortium cellular networks toeffectively support MTC (Machine Type Communication). The other direction is to design M2M-dedicated networksfrom scratch, which are often called low-power wide-area (LPWA) networks. We review, compare and categorize theproposals related to energy issues of cellular M2Mmainly over the period 2011–2015 for the first direction. Weintroduce the development of LPWA networks for the other research directions. We highlight that the cooperativerelaying, the design of energy-efficient signaling and operation, the new radio resource allocation schemes, and theenergy-efficient random access procedure are the main points of improvement. It is important to jointly use theaforementioned approaches, for example, joint design of random access control and radio resource allocation, to seekfor a trade-off between energy efficiency and other system performances.

Keywords: 5G, M2M, Energy-efficiency, LPWAN

1 IntroductionMachine-to-machine communication (M2M), also knownas Machine Type Communication (MTC), is an emerg-ing technology allowing devices to mutually communicatewithout (or only limited) human intervention, which isexpected to gain more popularity in the next decade andbe an integrated part of the future wireless networks[1, 2]. As an example, Ericsson estimates that 2 out of 50

*Correspondence: [email protected] of Network, Security and Multimedia, 2 Rue de la Chataigneraie,35510 Cesson Sévigné, France2IRISA, 263 avenue du Général Leclerc, 35042 Rennes, France

billion MTC devices in 2020 will be connected by cellulartechnology [3].MTC presents lots of its own characteristics different

from traditional human-to-human (H2H) or Human TypeCommunication (HTC): uplink-centric applications, shortbut more frequent transmission, large number of devices,difficulty to change battery, and so on [4]. Therefore,to well accommodate MTC traffic in the future wirelessnetworks, two possible approaches are envisaged:

• Design from scratch of M2M-dedicated networks,i.e., the emerging Low-Power Wide-Area Network

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(LPWAN). A representative example is theLoRaWAN (LoRa Wide Area Network) [5] proposedby LoRa Alliance [6]

• Evolution from existing wireless networks, whichconsists of adapting 3rd Generation PartnershipProject (3GPP) cellular networks to support MTCtraffic apart from HTC traffic, for example the LongTerm Evolution (LTE)-M [7].

Cisco estimates that the LPWA and evolved 3GPP net-works will have a dominant role for handling MTC trafficin the future. It is expected that 29 % of MTC deviceswill be served by the LPWA networks and 77 % of M2Mconnections will be served by the 3GPP networks (includ-ing 2G/3G/4G, shown in Fig. 1). The reason is that3GPP cellular networks, compared with LPWA networks,have ubiquitous coverage, largely deployed infrastructure,mature user subscription/management system, and so on.For both the aforementioned approaches, the chal-

lenges to be solved are the same: MTC subscription,network/overload control (also called massive access con-trol), security in M2M, diverse quality of service (QoS)provisioning, energy efficiency, etc. Recently, energyefficiency-related research has attracted more and moreattention, since it is deemed as a key performance indi-cator that determines if MTC is accepted as a promisingtechnology [8, 9]. Note that energy efficiency actually cov-ers the device side and network side. For cellularM2M, thenetwork side energy efficiency [10, 11] is not a principalconstraint; hence, it is not within the scope of this article.Instead, MTC devices are usually battery-operated, trans-mit small data, and require a long battery lifetime [12].The device side energy efficiency is a key problem tomake 3GPP cellular networks as a competitive solutionfor MTC. Thus, we put more focus on advance researchabout the cellular MTC energy-efficiency issue, especiallyin radio access networks.With regard to cellular M2M-related surveys, Taleb

and Kunz [13] focus on MTC devices subscription con-trol and network congestion/overload control. Chen andLien [14] talk about research efforts for efficient MTC

Fig. 1 Global M2M growth and migration from 2G to 3G and 4G.Source: [84]

and explore various M2M-related issues such as deploy-ment, operation, security, and privacy. Andres et al. [15]make a survey of proposals improving the operationof random access channel of LTE/LTE-A and evaluatethe energy consumption of LTE random-access proce-dure. Poncela et al. [16] identify the limitations of 4Gfor MTC (signaling, scheduler) and resume the improve-ments of LTE/LTE-A to handle M2M traffic. Severalreview papers [17, 18] discuss MTC in 3GPP LTE/LTE-Anetworks, introduce M2M use cases in detail, and iden-tify the challenges with regard to M2M over LTE/LTE-A,e.g., random access congestion, resource allocation withQoS provisioning. Wang et al. [19] survey and discuss var-ious remarkable techniques, in terms of all componentsof the mobile networks (e.g., data centers, macrocell, fem-tocell), towards green mobile cellular networks. Ismailet al. [20] investigate energy efficiency from the perspec-tive of network operators andmobile users. Yang et al. [21]make a survey about software-defined wireless network(SDWN) and wireless network virtualization (WNV) forthe future mobile wireless networks, which helps definethe future mobile wireless network architecture to tacklewith heterogeneous traffic.To our best knowledge, a comprehensive survey about

device side energy-efficiency issues in radio networksused for cellular M2M service is still not available in theliterature. Therefore, the goal of this article is to compareand categorize existing M2M-related energy-efficiencyproposals before discussing the trends for cellular M2Mresearch. In addition, in this article, we want to pro-vide a short overview of cellular M2M applications, detaildifferent types of classification of M2M services, and pro-pose a synthesis for the QoS demands. We also reviewthe advances of LPWAN, which are MTC-dedicated net-works, which today experience a rapid development. Therest of this article is organized as follows. Section 2presents the typical cellular M2M applications and severalclassifications according to different criteria and intro-duces a QoS requirement table for some typical cellularM2M applications. Section 3 compares the differencesbetween H2H andM2M in terms of traffic characteristics.Section 4 first talks about conventional M2M solutions incellular networks then presents the advance of referenceM2M network architecture. Section 5 resumes the devel-opment of LPWAN. Section 6 presents, categorizes, andcompares all found proposals related to energy issues forMTC in cellular networks. Section 7 gives the conclusionsobtained from this survey.

2 M2M applications classifications3GPP has listed a part of existing M2M applicationsshown in Table 1, including security, intelligent transportsystem, payment, health, remote maintenance/control,metering, and consumer devices. Besides, frequent M2M


Table 1 MTC applications categorization (non-exhaustive)according to [1, 17, 18]

Service area MTC applications

Security Surveillance systems

Backup for landline

Control of physical access

Car/driver security

Intelligent transport system Fleet management

Order management

Pay as you drive

Assess tracking

Navigation

Traffic information

Road tolling

Payment Point of sales

Vending machine

Gaming machines

Health Monitoring vital signals

Supporting the aged or handicapped

Web access telemedicine points

Remote diagnostics

Remote maintenance/control Elevator control

Lighting

Pumps

Industrial automation

Vehicle diagnostics

Metering Power/gas/water

Heating

Grid control

Industrial metering

Consumer devices Digital photo frame

E-book

Other futuristic applications Information ambient society

Robotic applications

Environment monitoring

applications are bicycle-sharing system [22], logisticsapplication, and insurance [22]. Given that M2M applica-tions are so various, it is impossible to develop a uniqueplatform to support all these applications in an economi-cal way. For example, the metering device is desired to besimple but the video surveillance device needs differentand powerful codec, needs different protocols, and shouldhave different radio capabilities. If only one solution orhardware platform is adopted for all the services, the

consequence is a MTC machine with a high complexity,which is like a smart phone today [23]. Thus, it is impor-tant to classify the existing M2M applications and pro-pose improvement according to different requirements. Inaddition, an appropriate M2M classification helps identifyQoS features and other works. In this section, we presentall classification schemes found in the literature, each ofwhich may serve for specific research purpose.

2.1 Classification according to reliability and quantity ofconnected machines

According to reliability and quantity of connectedmachines, the project METIS divides M2M applica-tions into two categories: mMTC and uMTC [24].mMTC refers to massive MTC and provides connectiv-ity for a large number of cost and energy-constraineddevices. Sensor and actuator deployments can be ina wide area for surveillance and area covering mea-surements, but also co-located with human users, asin body-area networks. The main attribute of this ser-vice is the massive number of connected devices, wherethe required rates decrease as the number of devicesgrows significantly. uMTC addresses the needs for ultra-reliable, time-critical services, e.g., V2X (vehicle-to-vehicle/infrastructure) applications and industrial controlapplications. Both examples require reliable communica-tion, and V2X additionally requires fast discovery andcommunication establishment. The main attribute is highreliability, while the number of devices and the requireddata rates are relatively low.

2.2 Classification according to the level of mobility anddispersion

According to the level of mobility and dispersion, M2Mapplications also could be categorized into four categories:dispersed and fixed application, dispersed and mobileapplication, concentrated and fixed application, and con-centrated and mobile application [22]. The dispersionrefers to the area that the M2M devices are spread outover. The mobility measures whether the device is sta-tionary or whether it needs to move around. The typicalexample for dispersed and fixed application is a smart gridwhere sensors are deployed in a large scale at a fixed loca-tion. Logistic applications are representative for dispersedand mobile application. For example, the sensors to trackthe cargo in the containermay spread andmove anywhere.Figure 2 shows the result of classification for some typicalM2M applications according to this criterion.

2.3 Classification according to delay tolerance levelAccording to the delay tolerance level, M2M applica-tions are divided into four classes: class 1 (elastic appli-cations), class 2 (hard real-time applications), class 3(delay-adaptive applications), and class 4 (rate-adaptive


Fig. 2M2M application taxonomy by mobility and dispersion. Source:[22]. This classification is not always true. Some applications may be indifferent cases in some situations

applications) [25]. The class 1 applications are generallyrather tolerant of delays, for example, file downloadingof remote MTC devices from MTC servers. The class 2applications need their data to be served within a givendelay constraint. The typical example of class 2 applicationis vehicle and asset tracking. Similar to class 2, the class 3applications are usually delay sensitive, but most applica-tions of class 3 can be made rather tolerant of occasionaldelay-bound violation and dropped packets. The class4 applications adjust their transmission rates accordingto available radio resources while maintaining moderatedelays.

2.4 Classification according to data reporting modeAccording to data reporting mode, M2M applicationsare classified into five categories [9, 26, 27]: time-driven, query-driven, event-driven, continuous-based,and hybrid-driven. Time-driven M2M applications referto those applications where machines periodically turn ontheir sensors and transmitters to transmit the collecteddata. Query-driven applications reply to certain instruc-tions from MTC application servers by transmitting data.This type of applications allows packet omissions, as adja-cent data reports usually contain redundant information.

Event-driven applications react to certain critical query orevent. Normally, applications fall into this category whenthey use priority alarm messages (PAM). Continuous-based M2M applications make the devices send their datacontinuously to the remote server at a pre-specified rate.Hybrid-driven is a combination of the aforementionedthree types.

2.5 QoS feature for typical M2M applicationsIn Table 2, a QoS requirement table in terms of datarate, latency, and message priority is given for some rep-resentative cellular M2M applications, based on differentreferences found in the literature.

3 M2M traffic characterization3.1 Comparison of M2M and H2H trafficA comprehensive comparison between M2M and H2His resumed in Table 3. The illustration of this differ-ence helps to rethink the design of some principles andthe optimization guideline. Here are some explanationsabout Table 3. First, the representative device in H2Hcommunication is a smartphone, which is equipped withmore and more computational capacity. The complex-ity of M2M devices is various: in application such asremote monitoring, it could be in format of sensor witha transceiver and a simplified processor. In intelligenttransport system (ITS), it could be regarded as a smart-phone without a screen. Second, the experiment resultsin [4] reveal that compared to H2H communication, cel-lular M2M traffic suffers from a higher packet loss ratio,and the reason may be the adverse deployment loca-tion and the lack of UI (e.g., screen) to show the signalstrength in its place. Third, since at present, a majoritycellular M2M applications are based on GSM/UniversalMobile Telecommunications System (UMTS) technolo-gies, MTC mainly supports short message service (SMS)or data reporting service. We could imagine more inno-vation M2M services when 4G network is largely rolledout.

Table 2 QoS feature and cellular M2M service. The values we propose are based on different references. We give only indicative values

M2M service Data rate Latency Priority

Surveillance system 64.000 b/s [86] Small Medium

Urgent notification Small Less than 1 s High

Fleet management Less than 500 B Very small High

Pay as you pay Small Very small High

Smart metering 500–1000 B per message 15 s–15 min [87] Low

Grid automation 10–100 kps [16] 0.1–2 s [16] High

Monitoring vital signals Less than 200 B per message [88] Small High

Monitoring in emergency Less than 200 B per message [88] Small High

Industrial automation Small Less than 5 ms [89] High

Vending machine control Small Small Medium


Table 3 Difference between M2M and H2H

Item M2M H2H

Delay range 10 ms∼several minutes [50] 250 ms (voice) to few seconds

(email for example)

Device composition GSM/UMTS/LTE module, GSM/UMTS/LTE/Wifi module

extension slots, USB, memory, CPU, etc. GPS, Bluetooth, USB, memory, CPU,

flash storage, etc.

Packet loss radio Relatively high [4] Low

Mobility Most of the M2M devices Humans are very rarely considered

(90 % according to [90]) fixed in practical mobile networks

are stationary.

Support service Mainly SMS or data reporting SMS/voice/Web/multimedia, etc.

Session duration Short but more or less frequent [91], Long but less frequent

/frequency depending on the applications:

monitoring, transport or others

Uplink MTC traffic is mainly generated in uplink Traditionally less traffic in uplink

but increase rapidly with the flourishing of interactive

applications such as social network

Downlink Less traffic except for some application Currently most traffic, for instance,

requiring interaction between sensors Web browsing and multimedia

and MTC servers, for example

consumer electronics use case

Message size Generally very short. Typically big, especially for multimedia and

In some cases could increase, real-time transmission

for example, if video sequences are uploaded

Number of devices Hundreds or thousands of devices per base station At most hundreds of UE,

typically tens of UEs per base station [15]

Battery life Up to a few years, Order of days or weeks,

requirement especially for deployment locations Human could easily recharge their device

with difficult access

Key metrics Energy efficiency, latency Delay, throughput, packet loss

for user experience

Not all MTC applications have the same characteristicsand not every optimization is suitable to all applications;therefore, features are defined to provide some structureto the customer and the network is then tuned accordinglyto needs.In many applications, saving energy for machines is

more important than increasing the throughput becausemachines usually transmit small data but have limitedelectric energy [12]. The energy efficiency is deemed asa key performance indicator that determines if M2M

communication is accepted as a promising communica-tion technology [8]. One of the important requirementsin cellular M2M system is extremely low power consump-tion [28]. The hard QoS guarantee is deemed as one of themost important requirements since disasters occur if tim-ing constraints are violated for some MTC applications[29]. Energy efficiency is the key in M2M communica-tions, since machine devices are generally powered bybatteries [9]. A critical issue in M2M communicationsis energy efficiency as typically the machine devices are


powered by batteries of low capacity and thus it is the keyto optimize their consumption [9].

3.2 M2M traffic-related research effortsThe first large-scale measurement aboutM2M traffic overan actual cellular network is in [4]. The possible impactsof MTC traffic on H2H traffic over cellular networks areevaluated in [30]. Traditionally, traffic models are classi-fied as source traffic model and aggregated traffic models[31]. Source traffic model is precise but not scalable withnumber of MTC devices, and aggregated traffic model isless complex but not precise. A coupleMarkov-modulatedPoisson process (CMMPP) model is proposed by combingthe respective advantages of source traffic and aggregatedtraffic models.

4 Conventional M2M in 3GPP networks andstandardization of M2M architecture

4.1 Conventional M2M solution in 3GPP networksThe 3GPP GSM cellular networks have been regarded asan ideal carrier for M2M, for the small data transmission,low data rate and energy efficiency, and low-cost hardwarefor MTC devices. Thus, lots of cellular-based commercialsolutions [32] have been proposed via GSM using SMS orGPRS before 2010. However, for a long-term view, GSMis not the best choice for MTC. There are many reasonsfor that. First, the operators have the plan of spectrumrefarming, that is, the spectrum resource will be allo-cated to the future generation of a cellular network with ahigher spectrum efficiency. For example, AT&T recentlyannounced the closure of its GSM networks in 2017 [33].Second, GSM is not able to handle the future massivenumber of MTC devices and cannot guarantee the QoSrequirements for some M2M applications. Third, GSMcan not satisfy the increasing demand for high data ratein M2M. Another limitation is that GSM requires MTCdevices initiating connections [34], which can not satisfythe device trigger requirement [35]. Thus, a shift from 2Gto 3G/4G or more advanced standards can be expectedin the next decade for the already deployed commercialM2M solution.The 3G family, UMTS and HSPA, is not a suitable tech-

nology for MTC because of the power efficiency andcost of the modem. Overall, it is an overkill technol-ogy in terms of design since it provides much more thanneeded [36]. As the roll out of 4G (mainly LTE and LTE-A)networks, the 4G technology is progressively attractive forMTC, among other reasons, the Orthogonal Frequency-Division Multiple Access (OFDMA) air interface allowsthe scaling of bandwidth according to needs. However,the modem cost and global coverage are still issues to besolved.As discussed in a previous section, M2M applica-

tions can be classified into four categories: time-driven,

query-driven, event-driven, and hybrid-driven. Inboth time-driven and event-driven types, the M2Mdevice initiates the communication and uploads thegathered data in the form of either SMS or packetdata. When M2M devices are self-triggered by anexpected event, they first send uplink preambles toestablish Radio Resource Control (RRC) connection.With the establishment of RRC connection, M2Mdevices connect to the core network (CN). Then,M2M devices establish connection with M2M server inTCP/application layer, which involve many transmissionoverhead.In query-driven, theMTC device responses to the query

from the MTC application server. For this kind of triggermechanism, 3GPP identifies three possible models: directmodel, indirect model, and hybrid model [37].

• Direct model. The first and the most straightforwarddeployment paradigm is the direct model, where theapplication server (AS) connects directly to anoperator network in order to communicate with theM2M devices without using the services of anyexternal service capability server (SCS)

• Indirect model. The second deployment paradigm isthe indirect model, in which the AS connectsindirectly to an operator network through theservices of an SCS in order to utilize additionalvalue-added services for M2M (e.g., control planedevice triggering)

• The third deployment paradigm is the hybrid model,where the AS uses the direct model and indirectmodel simultaneously in order to directly connect toan operator network to perform direct user planecommunications with the M2M devices while alsousing an SCS.

4.2 Standardization of reference M2M ArchitectureGiven that there is not a consensus about MTC refer-ence architecture, the Standards Developing Organiza-tions (SDO) and research community have proposed afew proposals. The European Telecommunications Stan-dards Institute (ETSI) provides a general M2M referencearchitecture with the purpose of designing an access andtransmission technology independent servicemiddle layer[38]. Nowadays, the architecture-related works are trans-ferred to oneM2M. The project oneM2M published theirreference architecture at the beginning of 2015, whichis similar but different than that of ETSI M2M. 3GPPproposes a MTC reference architecture with focus onimprovement of the core network [35]. IEEE 802.16pgives an overall architecture for M2M [39]. The authorsof [40] review and compare the aforementioned archi-tectures then propose a hybrid reference model. Sincethe reference architectures of 3GPP and IEEE 802.16p


are functionally equivalent, mainly the efforts of ETSI,oneM2M, and 3GPP are presented.

4.3 ETSI M2M reference architectureThe ETSI proposes a high-level reference architecture forM2M communication, which is illustrated in Fig. 3 andcomposed by a device-and-gateway domain and a networkdomain [38]. This architecture consists of the followingcomponents: (1) M2M-D, a device running M2M applica-tions usually embedded in a smart device and replies torequests or sends data; (2) M2M area network, a capillarynetwork (e.g., small-scale home environment) composedby individual M2M-D leveraging short-range communi-cation technologies (e.g. IEEE 802.15.1, Zigbee, Bluetooth,etc.); (3) M2M-G, a proxy responsible for interworkingfor M2M area network and network domain; (4) accessnetwork, a network that provides access to the core net-work for the devices (i.e. M2M-D and M2M-G) in thedevice-and-gateway domain, it can be, among others, inthe form of the access network of 3GPP, xDSL, satellite,and WiMAX; (5) core network, a network that providesvarious services such as IP connectivity, network control,interworking, and roaming between M2M-A and M2M-D. It includes, but is not limited to, 3GPP CN, ETSITISPAN CN, and 3GPP2 CN; (6) M2M-A, M2M appli-cations, application services that run the service logicand useM2M-SC via application programming interfaces;and (7) M2M-SC, a network node providing M2M func-tions toM2M-A and hiding network specificities forM2Mapplication development.

4.4 oneM2M reference architectureApart from the efforts of ETSI M2M, other regional SDOsconduct standardization activities. To avoid the risks of

Fig. 3 ETSI reference M2M communication architecture. (Modifiedbased on [38])

divergence, the oneM2M partnership project was estab-lished in 2012 by leading regional SDOs such as ETSI(Europe), TIA and ATIS (North America), CCSA (China),TTA (Korea), and ARIB and TTC (Japan). The objec-tive of oneM2M is to prepare, approve, and maintainglobally applicable, access-independent technical specifi-cations and reports related to M2M solutions, with initialfocus on the service layer. The functional architecture ofoneM2M is shown in Fig. 4. There exist mainly three func-tions: Application Entity (AE), Common Services Entity(CSE), and Network Services Entity (NSE). ApplicationEntity is an entity in the application layer that implementsan M2M application service logic and equivalent to theM2M-A in ETSI reference architecture (shown in Fig. 3).The Common Services Entity represents an instantiationof a set of common service function (i.e., M2M service sub-scription management, device management). A NetworkServices Entity hides the implementation of underlyingcommunication networks and provides services to theCSEs. The oneM2M reference architecture reuses princi-ples and solutions from ETSI M2M: the entity AE is likethe M2M-A in Fig. 3. The CSE is equivalent to the M2M-SC in Fig. 3. The infrastructure is similar to the ETSInetwork domain and the field domain is like the deviceand gateway domain of ETSI.

4.5 3GPP reference MTC architectureThe main contribution of ETSI M2M overall architec-ture is to standardize the resource structure representingthe information contained in M2M-SC, but ETSI hasnot specified the standardization for M2M area network,access network, and core network.The efforts of 3GPP about MTC architecture are sum-

marized in [35, 37]. Different from that of ETSI, thefocuses of 3GPP are mainly cellular wireless network,especially the access and core network. The proposedreference architecture is shown in Fig. 5.The enhancement made by 3GPP supports the device

trigger function. To this end, two new network nodes

Fig. 4 oneM2M reference functional M2M architecture. Source: [85]


Fig. 5 3GPP reference MTC architecture. (based on [35])

(MTC-IWF and SCS) and a series of reference pointsrelated to these two nodes are introduced. The first node,MTC Interworking Function (MTC-IWF), hides the inter-nal PLMN (Public Land Mobile Network) topology andrelays or translates signaling protocols used over Tsp(shown in Fig. 5) to invoke specific functionality in thePLMN. The main functions of MTC-IWF are to authorizethe SCS before communication establishment with the3GPP network, receive a device trigger request from SCS,select the most efficient and effective device trigger deliv-ery mechanism, etc. The SCS is an entity that connectsto the 3GPP network to communicate with MTC devicesand theMTC-IWF in theHPLMN. This entity offers capa-bilities to be used by one or multiple MTC Applications,and is controlled either by the mobile operator or a MTCservice provider.

4.6 M2M architecture in the literatureCombining the respective advantages of proposals of3GPP and ETSI, Lo et al. propose a cellular-centric M2Mservice architecture based on LTE-A specification [40],which is shown in Fig. 6. Their proposal is a four-tierarchitecture:

• Tier 1: the tier 1 consists of M2M applications andservers (namely M2M-A and M2M-S).

• Tier 2: the most distinguished change is at tier 2, inwhich a new functional entity M2M-R (M2M relayfunction) is introduced. M2M-R is an extension ofthe conventional LTE-A relay functionality. This

Fig. 6 Hierarchical LTE-A cellular-centric M2M service architecture.Source: [40]


extension enables LTE-A relay node to act as a M2Mdata concentrator.

• Tier 3: the most important functional entity at tier 3is M2M-G, which is actually a gateway to serve M2Mdevices non-3GPP compliant. The full-fledged 3GPPMTC-G implementation has not been standardizedant thus is still an open research issue.

• Tier 4: the tier 4 consists of those non-3GPPcompliant M2M devices (e.g., devices using ZigBee)

The proposal in [40] actually leverages M2M gateway(M2M-R) to support non-3GPP compliant devices. Theintroduction of the M2M-R aggregates the packets from alarge number of M2M devices into a single large packet,adds system capacity, and then reduces transmission.

5 Low-power wide-area networkThe term LPWA network refers to a network relying onlow-power and wide-area connectivity technology thatsimultaneously supports low batter energy consumptionand wide coverage area. The LPWA network is dedicatedto serve battery-powered applications characterized bylow throughput, delay tolerance, and being event-drivensuch as water-meter monitoring. Unlike the other tech-nologies that are adapted for Internet of Things (IoT),LPWA networks are purposely designed from scratch tomeet wide-area IoT application. LPWA technologies aretypically narrow-band (with some exceptions) and oper-ate in the ISM license-exempt spectrum bands. Facedwith a potential huge market, lots of players propose theirsolutions. Some typical and already deployed proprietarytechnologies of LPWA network are LoRaWAN, SIGFOX,Weightless, OnRamp, etc. A comprehensive comparisonabout these existing LPWA solutions is shown in Table 4.LoRa alliance has issued their first vision of LoRaWAN

specification [5] in January 2015, which is regarded as amajor step towards international standardization in thefield of LPWA networks. Thus, LoRaWAN technology istaken as a concrete example to give a general view aboutLPWA networks. The network architecture is illustratedin Fig. 7, which is a star-of-stars topology. A LoRaWANnetwork consists of the following components [5]:

• End-device: the end-device is the element in aLoRaWAN network which is responsible forcollecting and uploading information to remotenetwork server. LoRa supported functionalities canbe classified to three classes: class A (bi-directionalend-devices), class B (bi-directional end-devices withscheduled receive slots), and class C (bi-directionalend-devices with maximal receive slots). AllLoRaWAN end-devices at least support class A.According to applications, end-devices can optionallysupport class B and class C.

• LoRa air interface: The LoRa air interface providesthe connectivity between LoRa end-devices andgateway. It is on ISM (Industrial Scientific Medical)band and based on LoRa modulation, which is aproprietary modulation scheme. The LoRa data rateranges from 0.3 kbps to 50 kbps. The selection of datarate is a trade-off between communication range andmessage duration, and communications withdifferent data rates do not interfere with each other.

• LoRa gateway: the LoRa gateway receives thecommunications from the LoRa end-devices and thentransfers them to a network server via the backhaulsystem. Note that LoRa gateways may be co-locatedwith a cellular base station. In this way, they are ableto use spare capacity on the backhaul network.

• Network server: the LoRa network server managesthe network. The network server acts to eliminateduplicate packets, schedules acknowledgment, andadapts data rates (adaptive data rate scheme). Thecommunication between the LoRa gateway and thenetwork server is IP-based, and the underlying carriernetworks can be wired or wireless, Ethernet or 3GPPcellular, public or private networks.

In order to answer the huge expected demand of cel-lular M2M coverage, the standardization organizationsembarked on a process of standardizing narrow-bandtechnology for use in mobile spectrum. Two possibletracks are addressed by the 3GPP. The first track is theevolution of LTE 3GPP cellular system with the objec-tive of reducing the occupied bandwidth but still reusingthe basic LTE principles. The second track is to pro-pose a clean slate solution, which features narrow-band(NB) technologies and leverage the existing cellular infras-tructure. One major difference between these two tracksrelies in that whether it should redesign the radio inter-face and multiple access control mechanism for cellularM2M networks. As an effort in the first track, the 3GPPdeveloped LTE-M specification in Rel-12 [41] with intro-duction of a new low complexity device category (Cat-0).The device complexity of Cat-0 is 50 % of the previouslydefined Cat-1, which is the basic LTE terminal defined inthe first LTE Release (Rel-8). Nowadays, 3GPP is consid-ering to further optimize LTE-M in Rel-13: (1) bandwidthof 1.4 MHz and less complexity [41] and (2) a narrow-band evolution of LTE-M with bandwidth 200 kHz [7].For the clean slate solutions, the main idea is to sacri-fice the data rate in order to gain energy efficiency andcoverage extension. They are supposed to satisfy the fol-lowing requirements: deployment in a small bandwidth(e.g. 200 kHZ), ultra low-cost terminal (less than 5 dol-lars), ultra-long battery life, and coverage extension of 20dB with existing cellular technologies. The typical solu-tions include Narrow Band M2M (NB M2M), Narrow

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Table 4 The comparison among LPWAN solutions (All solutions are on ISM band, extracted from [92])

LoRaWAN NWave OnRamp SIGFOX Telensa Weightless-N Weightless-P Amber Wireless

Range 15–45 flat; 10 4 50 rural; Up to 8 5+ 2+ urban Up to 20

(km) 15–22 suburban; (but claims 25× 10 urban

(Caveat) 3–8 urban competition)

Band Spread; sub-GHz 2.4 GHz 868;902 868/915 Sub-GHz Sub-GHz 434, 868, 2.4GHz

(MHz) varies by region 470 (China)

Symmetric Depends on mode. No No(4:1) No Yes Uplink only Not yet determined

up/down Can be

Data rate 0.3–50 kbps 100 bps 8 bps–8 kpbs 100 bps low 30–100 kbps up to 100 kpbs up to 500 kbps

(adaptive)

Max nodes Depends; Million/base "10s of 1000s" Millions/hub 150,000/server 32767 NWs, 255 network

(Caveat) millions/hub (moving to 65535 hubs each, of 255 nodes

500,000) 16 M edge device

per NW

Operational Public or private Public or private Public or private Public Public Public or private Public or private

model (expect 80 % public)

Standard LoRa: proprietary Weightless-N No No No Yes In process

status LoRaWAN: yes (perhaps in future)

(if any)


Fig. 7 LoRaWAN network architecture. (based on [5])

Band OFDMA, and Cooperative Ultra Narrow Band (C-UNB) [42]. The deployment options include re-farmingGSM spectrum, LTE band guard, and leftover fragmentsof spectrum during re-farming of 2G/3G to 4G.When these standards will be available, the cellular

M2M connectivity solutions may be more competitive,since they not only fulfill the requirements of extendedcoverage and long battery life, but also have the advan-tage of being able to operate in currently existing cellu-lar network, thus requiring no additional deployment ofantennas, radio, or other hardware. On the contrary, theproprietary (at least for the time-being) technologies suchas Sigfox, On-Ramp, and Semtech require a dedicated net-work and maintenance team to deploy and maintain theirservices, which increases operational complexity for theoperator. However, their M2M solution is currently avail-able for the operators and starts to occupy some share ofthe market. In addition, some of the proprietary technolo-gies such as LoRa have the plan to adapt their technologyrunning on licensed spectrum and were submitted toGERAN [43] to keep their competitiveness.

6 Review about research proposals forenergy-related cellular M2M

To achieve energy efficiency at the device side, theresearch community has done lots of efforts. In thissection, we present, categorize, and compare all foundproposals related to energy issues for MTC in cellularnetworks. The energy issues may refer to energy saving,energy efficiency, or power efficiency/saving. Note that wejust concentrate on the research efforts about PHY/MAClayer. Obviously, the new routing algorithms [44, 45] helpto achieve energy efficiency, especially for mobile ad hocnetworks, but this is beyond the scope of this survey.In addition, there also exist mathematical works that arenot categorized into the following section, but they pro-vide useful design guidelines to help improve device sideenergy efficiency. For example, in [46], the transmission

energy is modeled as a function of transmission power,packet size and link capacity. A cumulative distributionfunction (CDF) of energy consumption for large-scaleMTC is derived by using stochastic geometry and Poissonpoint process. In [47], the comparison in terms of energyand power efficiency between uncoordinated and coordi-nated multiple access strategies are conducted. In [48],the work [47] is extended by considering various packetsize and imperfect power control. The result of classifi-cation and comparison among the proposals presented inthis survey is resumed in Table 5.

6.1 Cooperative relayingCooperative relaying, also called cooperative design [49],can be interpreted as the process of devices helping eachother to jointly achieve a goal more efficiently than eachdevice could do on its own. The first possible formis group-based and relay (in fact, at most two hops)mechanism, which is very useful for energy-saving, mas-sive access control. A general and common descriptionabout group-based and relay transmission is illustratedin Fig. 8: all MTC devices are classified into severalgroups (some references call group as cluster). A certaindevice in each group is selected as the coordinator (alsocan be called cluster header, group head) according tosome criteria (e.g., QoS requirements [50], link quality,or location). The MTC devices other than the coordi-nators transmit packets to their allocated coordinators,which relay the received packets to the BS (multiple-hop communication). In this model, there exist two links:MTCD-to-coordinator (actually MTCD-to-MTCD link,since the coordinator is by nature still a MTC device)and coordinator-to-BS. A basic issue is the interferencebetween the two aforementioned links. This problem canbe addressed by interference-based topology control algo-rithm [51], specific scheduling, the use of different fre-quencies between the two communications (but this ispricey in frequencies), or even the use of two differentprotocols as proposed in [12]. In addition, how to enableandmake theMTCD-to-MTCD link efficient is addressedby device-to-device (D2D) communication [52, 53] andmultiple-hop communication [54].The idea of group-based communication is presented

in [55] but without clarifying grouping/clustering algo-rithms. In fact, the algorithms of device grouping andthe coordinator are a key factor influencing energy effi-ciency. A series of K-means (K-means clustering aims topartition n observations into k clusters in which eachobservation belongs to the cluster with the nearest mean)which derived grouping and coordinator selection algo-rithms are proposed in [56]. Ho and Huang [12] proposea two-stage mechanism to minimize the energy consump-tion of all MTC devices. The first stage consists of MTCdevice grouping and coordinator selection. The criteria

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Table 5 Categorization and comparison of energy/power saving-related proposals

Category Subcategory Reference Principle for energy saving Drawback Notes

Cooperative Clustering and relay [55] Group devices into clusters; High energy consumption It is possible to combine

design [56] Cluster head relays the messages for for cluster head; cooperative relaying with

[12] other cluster member in the same Scheduling and resource issues in other emerging technologies

[57] group order to manage the interference; for the device-to-device

Delay increase cause of relay link

Cooperation between MTC [9] Adjust MTC device setting High complexity for MTC devices

server and MTC devices according to context

M2M gateway [23] Similar to clustering and Installation and deployment Reduce implementation complexity

[58] relay, except that M2M gateway cost of M2M gateway for MTC device.

[60] is a special network node for operators Possible to use LoRa gateway

instead of a MTC device as M2M gateway.

Design of Modified DRX and [62] Make MTC devices stay in low High delay Simple method to

energy efficient Idle state [63] power mode as long as possible achieve energy saving

signaling and

operation

Extending paging cycle [1] Make MTC devices stay in low High delay

[2] power mode as long as possible

[64]

Reduction of RRC [65] Make MTC devices stay in low Impact on H2H service

Inactivity timer power mode as long as possible

Group-based and [66] Group paging for MTC devices May reduce the paging capacity

M2M- dedicated of H2H;

paging mechanism Scalability and bacward-

compatibility issues.

Removal [66] Remove activities related to Applicable uniquely for Reduce the cost of MTC

of unnecessary activities mobility management (MM) M2M application with no device and energy

for MTC Device or low mobility consumption

Disconnect MTC-device [67] Instead of staying in low power mode, High delay

from network when inactive turn devices radio off

Radio resource Formulation of [71] Convert radio resource allocation High complexity;

allocation optimization problem [72] into an optimization problem with Scalability issues;

and packet constraints Possible impact for human

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Table 5 Categorization and comparison of energy/power saving-related proposals (Continued)

Category Subcategory Reference Principle for energy saving Drawback Notes

scheduling users.

Optimized with periodicity [73] Leverage the periodicity of MTC Applicable uniquely to periodic

[74] M2M applications;

Not easy applied in the presence

of different period values.

Packet scheduling [76] Propose packet scheduling Compatibility with H2H; Ensure QoS requirements

[29] adapted for cellular MTC context May need modifications of the specification

Energy efficient New random access protocol [15] ALOHA-based random It may be difficult to find a specified

random access access protocol is not random access protocol suitable

and MAC the best option for MTC for all cellular users

Fixed time alignment [78] Leverage the low mobility of MTC Only applicable for M2M Reduce the access collision

with no mobility

Transmit message in [23] Transmit message directly Scalability issues Reduce the signaling overhead

MAC PDU or preamble in access reservation stage


Fig. 8 Illustration of M2M devices clustering

for grouping and coordinator selection in each group arethe minimization of energy consumption of this group.The second stage is that BS performs power allocationfor each coordinator to further reduce energy consump-tion. However, it is difficult to obtain the closed-formsolution for the formulated problem, the proposal of [12]could achieve suboptimal result. An implementation ofclustering and relay is presented in [57]: intra-clustercommunication uses CSMA/CA protocol with multiple-phases while resource reservation-based protocol is usedfor communication between the cluster head and BS. Thedrawback of the group-based and relay design relies inthat (i) although it is globally energy efficient for all theMTC devices, the cooperative relaying causes the clusterhead to consume more energy than others; (ii) each MTCdevice should be equipped with multiple transceiver (e.g.,OFDMA transceiver for coordinator-to-BS link, TDMAtransceiver for MTCD-to-coordinator link), since everydevice is possible to be selected as the coordinator.The second form of cooperation is to introduce M2M

gateway (may be called proxy [23]). The M2M gate-way serves as an intermediary node to collect and pro-cess data for neighbor MTC devices. Thus, topologically,M2M gateway is very similar to the aforementioned clus-ter header, except that M2M gateway does not have itsown data to transmit and may have a permanent energysource. It is preferred to use half-duplex M2M gateway toavoid self-interference and reduce implementation com-plexity [25]. The use of M2M gateway helps reduce thenumber of accessing devices, signaling overhead and con-tention, and thus helps improve energy efficiency. Chenand Wang [23] give a simple work flow for MTC withM2M gateway. Pereira and Aguiar [58] consider the useof smartphones as M2M gateway between the BS andMTC devices. The existing scheduled airliners used asrelays between ground devices and satellites are presented

in [59]. It is also possible to add a gateway-like elementnode called M2M facilitator [60] between 3GPP RAN andcore network, which is shown in Fig. 9. M2M devicesnominally communicate with the M2M server. However,the M2M devices only communicate with the base stationand enter in sleepmode after the session with the base sta-tion. The base station then transfers received data to theM2M facilitator. Finally, the M2M facilitator is in chargeof data transmission, retransmission, and session termina-tion with the M2M server. Since the devices communicateonly with the M2M facilitator and the latter has no energyconstraint, the protocol stack at the device side can besignificantly simplified to save energy consumption. Thecost is that a fraction of protocol stack complexity is trans-ferred to the M2M facilitator. The inconvenient pointsof M2M gateway-related proposals are: (i) the opera-tor should deploy M2M gateways to serve MTC devices,whichmay be of high cost and (ii) not flexible and dynamiccompared with a clustering-based group.Compared with the group-based and relay mechanism,

the use of M2M gateway may be more energy efficientfor devices. This is actually due to that a part of energyconsumption of MTC devices is transferred to the M2Mgateway, which may have a permanent energy source.The third possible form of cooperation mechanism is

context-based communication between the MTC serversandMTC devices. The philosophy of this axis is intelligentalgorithms can be deployed at application level to helpM2M devices and overall network adjust their settingsto improve energy efficiency. According to this philos-ophy, the authors propose a context-aware frameworkbased on the context concept in [9]. M2M devices sendcontext-related information to the MTC server, and theMTC server judges the data reporting mode (time-driven,query-driven, event-driven, or hybrid) and QoS feature(real-time, priority, and accuracy) then returns a set ofoutput useful for the setting device’s behavior. This outputinvolves inter-arrival time, average packet size, transmit-ting power, packet omission, etc. that could extend theoperative lifetime of M2M devices.

6.2 Design of energy-efficient signaling and operationAn intuitive solution to achieve energy-efficiency forMTC is to design energy-efficient signaling and simplify

Fig. 9 Un-peer-to-peer cellular MTC architecture with the M2Mfacilitator. Source: [60]


the operations leading to energy wasting. The key philos-ophy is to maximize the duration in a low-power stateand reduce or even delete all operations unnecessary forMTC. The improvement possibilities are summarized asfollows:

(1) Adaption of Discontinuous Reception (DRX) and idlestate: 3GPP has incorporated some energy/power-saving mechanisms into their specification for H2Hdevices, for instance, DRX [61]. Due to long inter-arrival time between traffic sessions, MTC devicesstay in LTE idle states most time as it is designed as alow-power state. In the idle state, MTC devices go tosleep to save battery and wake up periodically toinquire any system information (SI) update ordownlink packet arrival via paging mechanism.However, the aforementioned mechanisms are stillinsufficient for MTC device, and DRX and idle stateare adapted to improve device power saving in [62].The potential of trading high delay for reducingMTC devices battery consumption is studied in [63].

(2) Extending paging cycle: 3GPP considers to extendpaging cycle for M2M devices [1, 2, 64], butsimulation results show that extending paging cyclebeyond 2.56 s reduces the power consumptionsignificantly for M2M traffic with small or mediumvalues of inter-arrival time, but has no effect whenthe paging cycle is more than a limit [65]. In addition,extension of the paging cycle always increases thepacket buffering delay.

(3) Reduction of RRC inactivity timer: the network keepsMTC device in connected mode even after the lastpacket delivery due to the RRC inactivity timer inwhich MTC device still keep a high power, thus theimpact of variation of RRC inactivity timer isexplored and proved to be better than extendingpaging cycle [65].

(4) Group-based and M2M-dedicated pagingmechanism: to increase paging capacity, multipleterminals are allocated with the same pagingoccasion (PO). H2H and M2M terminals may occupythe same certain paging occasion. If both are pagedtogether, a large number of H2H terminals and MTCdevices need to wake up at the same PO, whichresults in terminal power wasting. The solution toavoid this kind of power wasting is paging targetswith group ID or device ID at dedicated pagingoccasion allocated uniquely for M2M devices [66];

(5) Removal of unnecessary operations: even in the idlestate, the MTC devices are not actually inactive: theyare supposed to run activities related to mobilitymanagement (e.g., TAU procedure for LTE). SinceMTC presents low mobility feature, it is possible toremove these unnecessary operations (periodic AS

measurement and NAS LAU/RAU/TAU procedure)for power saving [66].

(6) Disconnect MTC device from network wheninactive: since the device power consumption in idlestate is not negligible, it is possible to turn the LTEradio off (i.e., disconnect the device from thenetwork) to obtain further power saving [67] and thisis proved to be outperforming than the approach ofreducing RRC inactivity timer. However, the gain ofpower saving is with cost of higher delay related todownlink packet. In addition, the network may alsoneed to store the downlink data, and the MTC deviceidentities if the MTC device is off.

6.3 Radio resource allocation and packet schedulingstrategies

Radio resource allocation and packet scheduling strategiesplay a key role in the overall performance of OFDMA-based wireless networks [68]. Most of the research effortsin this field are about the downlink, which can for exampleimprove network throughput and mitigate inter-cell inter-ference [69, 70]. However, they have limited effectivenessabout energy efficiency on the MTC device side. In addi-tion, MTC applications are usually uplink-centric; thus,it is import to design new radio resource allocation andpacket scheduling schemes to achieve energy-efficiency.Zheng et al. [25] study the radio resource allocation

scheme for the five possible links in an M2M and H2Hco-existence scenario: the eNB-to-UE link, the eNB-to-MTCD link, the eNB-to-MTCG link, the MTCG-to-MTCD link, and the MTCD-to-MTCD link. They firstdesign a new radio resource partition scheme and thenpropose efficient radio resource allocation algorithms ineach partition to mitigate co-channel interference andenhance network efficiency, which is useful for energy-efficiency, but they just consider the data rate whenallocating resource for both UE and MTC devices.Given that MTC usually features low data rate and

emphasizes the delay requirement, Aijaz and Aghvami[71] extend the work [25] by formulating a bits-per-joule capacity maximization optimization problem. Theresource constraint for UE is the minimum data rate andmaximum tolerable packet delay for MTC devices. Theypropose two heuristic algorithms based on the steepestdescent approach to solve the optimization problem. Aijazet al. [72] further extend [71] by introducing the notionof statistical QoS (i.e., probability of exceeding a specificdelay threshold) and solve the optimization problem withcanonical duality theory (CDT).Radio resource allocation scheme can also leverage the

periodicity of MTC, since a considerable M2M usersrepetitively access the networks to transmit collected data.Zhang [73] proposes to use persistent resource allocationfor periodic M2M applications and indicate the condition


for multiplexing multiple MTC devices with differentreporting periods. Madueno et al. [74] propose a peri-odically occurring pool of resources that are reservedfor M2M communications and shared for uplink trans-mission by all MTC devices. Song et al. [75] proposea multiple-period polling service in LTE transport net-work to avoid random access procedure by leveraging theperiodicity feature of M2M.Group-based feature can be leveraged when design-

ing M2M-compatible radio resource allocation strategies.Since MTC devices in the same cluster are assumed tohave exactly the same QoS requirements, a grouping-based radio resource allocation algorithm [29, 50] is pro-posed for LTE-A base stations according to packet arrivalrate and maximum jitter. The access grant time interval(AGTI) is periodically allocated for eachMTCdevice clus-ter according to cluster priority. All the MTC devices ofa same cluster occupy an equal number of resource block(RB) in allocated AGTI. The shortcomings of this proposalare as follows: (i) the number of served MTC devices islimited due to the inefficient utilization of resource; (ii)the base station only supports a unique packet size for allMTC devices; (iii) the proposal is not scalable since BShas to know in advance how many clusters are there in itscoverage; and (iv) the supported QoS classes are limited.Since the packet delay employed in [29] is a determin-

istic bound, Gotsis et al. [76] extend the work of [29] byusing a statistical QoS (also used in [71]), which refersto the probability of exceeding a specific delay threshold.They propose an analytical model to study the perfor-mance of period scheduling algorithm in terms of statisti-cal QoSmetric with modeling the arrival traffic as Poissonprocess. They also enhance the periodic scheduling in [29]with queue-awareness in which devices with larger queuesthan the others are first granted access to the scheduledAGTI with an extra cost in complexity and signaling.To overcome the issue of QoS classes, two uplink packet

scheduling strategies are proposed in [77], which takeinto account both the channel conditions and the maxi-mum allowed delay of each device; however, it suffers fromincreased signaling requirements and is just able to servelimited number of MTC devices. In [12] a resource alloca-tion scheme (i.e., optimal transmit power allocation overRBs) for M2M traffic over OFDMA frames is proposed,assuming a two-hop access to the LTE network though acoordinator. This work achieves the reduction of energyconsumption but does not take into account QoS issue ofMTC such as the delay requirements.

6.4 Energy-efficient random access procedure andMACThe currently standardized random access procedure, forexample, in LTE networks, is designed and optimized forlarge amount of data transmission and limited UEs; thus,it suffers from a random access overload issue which leads

to high collision probability and waste of energy. Theimprovement works about random access procedure havebeen attracting the attention of the research community.Current random access optimization research efforts canbe resumed into two categories: (i) improvement for cur-rently employed ALOHA random access procedure and(ii) designs of a non-ALOHA procedure.For the first category, the focus is to reduce either sig-

naling overhead for small data transmission or contentionprobability, since both reduce the transmitted bits forMTC devices and thus help energy saving. For MTC withlots of fixed-location machines, Ko et al. [78] proposea novel random access scheme based on fixed TA (tim-ing alignment) for a OFDMA-based cellular system. Theproposal is based on the assumption that the TA valuebetween each fixed-location machine device and eNB isfixed and unchanged, and the MTC devices store the TAvalue acquired from the initial RA access and compare itwith the TA values obtained from the subsequent randomaccess procedure. In case of mismatch of TA values, MTCdevices directly start the retransmission procedure toavoid possible collision after waiting a randomly selectedbackoff time. Otherwise, the devices continue the conven-tional procedure. However, this proposal is only applicableto M2M with stationary devices and has a limited effecton energy efficiency.For M2M applications with small data transmission,

establishing RRC connection and network connection totransmit several bits is deemed as wasteful. Thus, Chenand Wang [23] suggest either (i) use the MAC PDU thatshould carry the RRC signaling to carry the data and(ii) define a special preamble to transmit coded data.The drawbacks of [23] are as follows: (i) the solutionbased on the preambles is not very scalable due to thelimited amount of available preambles and (ii) for a long-term view, the transmission of data in the control planeviolate the principle of separation between the controlplane and the data plane. Wiriaatmadja and Choi [79]propose to simplify the data communication procedureby allowing MTC devices to send data right after thepreamble transmission without explicitly establishing aconnection.For the congestion in random access, Physical Downlink

Shared Channel (PDSCH) resources of LTE are deemedsufficient in most communication scenarios. To ease thecongestion on the air interface, those downlink assign-ments and uplink grants for MTC devices, which can-not be served by Physical Downlink Control Channel(PDCCH), can be aggregated into a transport block onPDSCH identified by a special radio network terminalidentifier (RNTI) called MTC-RNTI [80]. MTC devicesmonitor PDCCH channel with their own cell RNTI andMTC-RNTI simultaneously. Game theory has been usedfor the context of cellular M2M to optimize preamble


allocation [81]. In addition, a detailed random accessrelated proposals are summarized in [15], which can be acomplement of our categorization.Random access protocols can be categorized into two

families: ALOHA family and tree family [82]. Andreset al. [15] claim that ALOHA-based RACH procedureis not suitable for MTC. Instead, they mention thatRACH procedure based on distributed queuing (DQ) ismore promising. The concept of DQ [82] was proposed20 years ago and then demonstrated in other literaturesin terms of stability and near-optimum behavior. DQ isbased on the combination of a m-array tree splittingalgorithm with a smart set of simple rules that allow orga-nizing every device in one out of two virtual queues.Due to the rules of DQ, it behaves as a random accessmethod for low traffic loads, and it switches smoothlyand seamlessly to a reservation access method as thetraffic load increases. The authors conduct some ongo-ing research efforts applying DQ ideas within LTE/LTE-Asystems. Dhillon et al. [47] suggest to implement a load-dependent access scheme wherein uncoordinated strategyis for light load and coordinated strategy for heavy load.Bontu et al. [83] propose a new uplink (UL) physical,transport, and logical channel: common traffic channel(CTCH), UL simultaneous-access shared channel (UL-SSCH), and physical uplink simultaneous access sharedchannel (PUSSCH). The aforementioned channels enableM2M devices to simultaneously transmit data packet inthe same radio resource. They also propose to transmitcontrol signaling through in-band transmission in the userplane control.In fact, the cellular network evolution trend is always

to seek for a trade-off between diverse performance met-rics such as energy efficiency, packet delay, and user datarate. Hence, the gain of energy efficiency is inevitablywith cost of a certain degradation of other performancemetrics. For example, the cooperative relaying achievesenergy efficiency with more packet delay due to multiple-hop transmission. The design of energy-efficient signal-ing and operation, such as disconnection of the MTCdevices from a network when inactive, surely saves energyconsumption but introduce higher delay for downlinkpacket, and the effort in this direction is not systematic.The energy-efficient uplink radio resource allocation andpacket scheduling schemes provide a systematic mannerto gain energy efficiency for MTC, but they may leadto either serving less number of MTC devices and sup-porting limited QoS classes or bringing more signalingmessages in radio access networks. More importantly,it is difficult to design schemes simultaneously satisfy-ing the QoS provisioning for both human and MTCusers. The random access can be designed to reduce theretransmission probability for MTC users, but humanusers may suffer from degradation of service, due to

the limited radio access resources. Therefore, it is veryimportant to jointly apply the aforementioned approachesto gain the device side energy efficiency and seek fora trade-off with other system performance. For exam-ple, the random access procedure can be optimized byapplying cooperative relaying to reduce the direct linkstowards the base station. The energy-efficient uplink radioresource allocation algorithms can be jointly designedwith a random access procedure: allocate more resourcesfor PRACH in case of overload or limit the num-ber of access devices when no radio resource for datatransmission, etc.

7 ConclusionsThe arrival of billions of connected machines in the shortand mid terms is a huge challenge for cellular networks,although not all these machines will necessarily be con-nected to the latter. A part of the machines will only beconnected to the ad hoc networks between each other,while some other will rely on the dedicated networks ofLPWAN style. Yet, a large part of the machines will be bet-ter served within 3GPP cellular networks, especially thefuture 5G networks. Nowadays, 2G-style GSM or GPRScellular is often used for cellular M2M service. However,GPRS is not suitable for M2M and often not competitivecompared with LPWA solution, since it can not supporta large number of devices, among other reasons. LPWAtechnologies and cellular 3GPP solutions will be the mainsupport used for cellular M2M. In this paper, we describethe present state of these technologies and the evolutionsas expected today.We propose a synthesis for the QoS demands and the

difference of characteristics between H2H and M2M. Wethen review the proposals for radio coverage and ser-vice of these machines. We identify the advantage ofcellular networks for this expected service. The 3GPPcellular networks have a mature infrastructure to pro-vide a wide-coverage, high-availability service and usersubscription/management system, but the shortages of3GPP networks are relatively high energy consumptionlevel and cost of hardware with regard to LPWA net-works. These challenges are addressed by some researchproposals that we summarize in this article. In terms ofLPWA network such as LoRa, their significant advan-tage is their low energy consumption design and low-costhardware. However, their disadvantage is that the opera-tors should deploy dedicated infrastructure for providingLPWA-related service.According to our survey, to improve the device side

energy efficiency in the future cellular networks, we getsome design guidelines as follows:

• The possible approaches to improve device sideenergy efficiency for cellular MTC include the


following: cooperative relaying, design of energy-efficient signaling and operation, radio resourceallocation and packet scheduling strategies, andenergy-efficient random access procedure and MAC.

• It is a better solution to employ cooperative relaying,since it can be combined with other emergingtechnologies such as D2D communication, ad hocnetworks research results, and LoRa technology.

• The radio resource allocation and packet schedulingschemes allows to get energy efficiency while keepinga certain level of QoS; however, it is difficult to designthis kind of schemes simultaneously satisfying theQoS provisioning for both human and MTC users.

• No matter by which approach, to gain energyefficiency is always with sacrifice of other systemperformances such as packet delay. Thus, it isimportant to jointly use the aforementionedapproaches, for example, joint design of randomaccess control and radio resource allocation, to seekfor a trade-off between energy efficiency and othersystem performances.

AcknowledgmentsThe authors would like to thank Professor Michela Meo, from Politecnico diTorino, for the fruitful discussions about this work.

Competing interestsThe authors declare that they have no competing interests.

Received: 20 February 2016 Accepted: 11 May 2016

References1. 3GPP, Service requirements for machine-type communications, TS 22.368

V11.0.0 (2010). http://www.3gpp.org/DynaReport/22368.htm2. 3GPP, Study on RAN improvements for machine-type communication. TS

37.868 V11.0.0 (2010). http://www.3gpp.org/DynaReport/37868.htm3. Ericsson, More than 50 billion connected devices. White paper (2011).

http://www.akos-rs.si/files/Telekomunikacije/Digitalna_agenda/Internetni_protokol_Ipv6/More-than-50-billion-connected-devices.pdf

4. MZ Shafiq, L Ji, AX Liu, et al., A first look at cellular machine-to-machinetraffic: large scale measurement and characterization. SIGMETRICSPerform. Eval. Rev. 40(1), 65–76 (2012)

5. N Sornin, M Luis, T Eirich, T Kramp, Lorawan specification. TechnicalReport V.1.0, LoRa Alliance (2015). https://www.lora-alliance.org/portals/0/specs/LoRaWANSpecification1R0.pdf

6. LoRa Alliance. https://www.lora-alliance.org. Accessed 14 Oct 20157. R Ratasuk, N Mangalvedhe, A Ghosh, B Vejlgaard, in Vehicular Technology

Conference (VTC Fall), 2014 IEEE 80th. Narrowband lte-m system for m2mcommunication (IEEE, 2014), pp. 1–5

8. R Lu, X Li, X Liang, X Shen, X Lin, GRS: The green, reliability, and security ofemerging machine to machine communications. IEEE Commun. Mag.49(4), 28–35 (2011)

9. JM Costa, G Miao, in INFOCOMWorkshops. Context-awaremachine-to-machine communications (IEEE, 2014), pp. 730–735

10. L Suarez, L Nuaymi, J-M Bonnin, An overview and classification of researchapproaches in green wireless networks. Eurasip J. Wirel. Commun. Netw.2012, 1–18 (2012)

11. C-Y Wang, C-H Ko, H-Y Wei, AV Vasilakos, A voting-based femtocelldownlink cell-breathing control mechanism. IEEE/ACM Transactions onNetworking. 24(1), 85–98 (2014)

12. CY Ho, C Huang, Energy-saving massive access control and resourceallocation schemes for M2M communications in ofdma cellular networks.IEEE Wirel. Commun. Lett. 1(3), 209–212 (2012)

13. T Taleb, A Kunz, Machine type communications in 3GPP networks:potential, challenges, and solutions. IEEE Commun. Mag. 50, 178–184(2012)

14. K-C Chen, S-Y Lien, Machine-to-machine communications: technologiesand challenges. Ad Hoc Netw. 18, 3–23 (2014)

15. L Andres, A Luis, AZ Jesus, Is the random access channel of LTE and LTE-Asuitable for M2M communications? a survey of alternatives. IEEECommun. Surv. Tutor., 4–16 (2014)

16. J Poncela, JM Moreno-Roldan, M Aamir, B Alvi, M2M challenges andopportunities in 4G. Wirel. Pers. Commun., 1–14 (2015)

17. F Ghavimi, H-H Chen, M2M communications in 3GPP LTE/LTE-A networks:architectures, service requirements, challenges, and applications. IEEECommun. Surv. Tutor. 17(2), 525–549 (2015)

18. Y Mehmood, C Görg, M Muehleisen, A Timm-Giel, Mobile M2Mcommunication architectures, upcoming challenges, applications, andfuture directions. EURASIP EURASIP J. Wirel. Commun. Netw. 2015, 1–37(2015)

19. X Wang, AV Vasilakos, M Chen, Y Liu, TT Kwon, A survey of green mobilenetworks: opportunities and challenges. Mob. Netw. Appl. 17(1), 4–20(2012)

20. M Ismail, W Zhuang, E Serpedin, K Qaraqe, A survey on green mobilenetworking: from the perspectives of network operators and mobileusers. IEEE Commun. Surv. Tutor. 17(3), 1535–1556 (2015)

21. M Yang, Y Li, D Jin, L Zeng, X Wu, AV Vasilakos, Software-defined andvirtualized future mobile and wireless networks: a survey. Mob. Netw.Appl. 20(1), 4–18 (2015)

22. OECD, Machine-to-machine communications: connecting billions ofdevices. OECD Digital Economy Papers, No. 192 (2012).doi:10.1787/5k9gsh2gp043-en

23. Y Chen, W Wang, in Vehicular Technology Conference Fall (VTC 2010-Fall),2010 IEEE 72nd. Machine-to-machine communication in LTE-A (IEEE,2010), pp. 1–4

24. H Tullberg, P Popovski, et al., in IEEE Communications Magazine. METISsystem concept: The shape of 5G to come, (2015). https://www.metis2020.com/wp-content/uploads/publications/IEEE_CommMag_2015_Tullberg_etal_METIS-System-Concept.pdf

25. K Zheng, F Hu, W Wang, et al., Radio resource allocation in LTE-advancedcellular networks with M2M communications. IEEE Commun. Mag. 50(7),184–192 (2012)

26. JN Al-Karaki, AE Kamal, Routing techniques in wireless sensor networks: asurvey. IEEE Wirel. Commun. 11(6), 6–28 (2004)

27. LM Borges, FJ Velez, AS Lebres, Survey on the characterization andclassification of wireless sensor network applications. IEEE Commun. Surv.Tutor. 16(4), 1860–1890 (2014)

28. IEEE802.16, Machine to machine M2M communication study report. Ts.(2010). http://www.ieee802.org/16/ppc/docs/80216ppc-10_0002r7.doc

29. S Lien, K Chen, Massive access management for QoS guarantees in 3GPPmachine-to-machine communications. Commun. Lett., IEEE. 15(3),311–313 (2011)

30. K Zheng, S Ou, J Alonso-Zarate, M Dohler, F Liu, H Zhu, Challenges ofmassive access in highly dense lte-advanced networks withmachine-to-machine communications. IEEE Wirel. Commun. 21(3), 12–18(2014)

31. M Laner, P Svoboda, N Nikaein, M Rupp, inWireless CommunicationSystems (ISWCS 2013), Proceedings of the Tenth International SymposiumOn.Traffic models for machine type communications (VDE, 2013), pp. 1–5

32. I Curran, S Pluta, inWater Event, 2008 6th Institution of Engineering andTechnology. Overview of machine to machine and telematics, (2008),pp. 1-33

33. AT&T, Frequently Asked Questions Regarding 2G Sunset (2014). http://www.business.att.com/content/other/2G_Sunset_FAQs_2014_A1.pdf.Accessed 27 Oct 2015

34. M Martsola, T Kiravuo, J Lindqvist, inMobile Technology, Applications andSystems, 2005 2nd International Conference On. Machine to machinecommunication in cellular networks (IEEE, 2005), p. 6

35. 3GPP, System improvements for machine-type communications. TS23.888 V11.0.0. (2012)

36. CA-H Dohler, (ed.), inMachine-to-machine (M2M) Communications.Chapter 3 - overview of 3GPP machine-type communicationstandardization (Woodhead Publishing, Oxford, 47)

http://www.3gpp.org/DynaReport/22368.htm




https://www.lora-alliance.org/portals/0/specs/LoRaWAN Specification 1R0.pdf

https://www.lora-alliance.org/portals/0/specs/LoRaWAN Specification 1R0.pdf

https://www.lora-alliance.org

http://dx.doi.org/10.1787/5k9gsh2gp043-en

https://www.metis2020.com/wp-content/uploads/publications/IEEE_CommMag_2015_Tullberg_etal_METIS-System-Concept.pdf



http://www.ieee802.org/16/ppc/docs/80216ppc-10_0002r7.doc

http://www.business.att.com/content/other/2G_Sunset_FAQs_2014_A1.pdf

http://www.business.att.com/content/other/2G_Sunset_FAQs_2014_A1.pdf


37. 3GPP, Architecture enhancements to facilitate communications withpacket data networks and applications. Technical Report TS 23.682 V12.4.0. (2015). http://www.3gpp.org/DynaReport/23682.htm

38. ETSI, Machine-to-machine communications (M2M); functionalarchitecture. TS 102 690, ETSI. (2011)

39. IEEE802.16p, Machine-to-machine (M2M) system requirementsdocument. Technical Report. IEEE 802.16 Broadband Wireless AccessWorking Group (2011). http://ieee802.org/16/m2m/#10_0004

40. A Lo, Y Law, M Jacobsson, A cellular-centric service architecture formachine-to-machine (M2M) communications. Wirel. Commun., IEEE.20(5), 143–151 (2013)

41. Nokia, LTE-M—optimizing LTE for the Internet of Things. White paper(2015). http://networks.nokia.com/sites/default/files/document/nokia_lte-m_-_optimizing_lte_for_the_internet_of_things_white_paper.pdf

42. 3GPP, Cellular system support for ultra-low complexity and lowthroughput Internet of Things (CIoT). TR 45.820. 3rd GenerationPartnership Project (3GPP) (2015). http://www.3gpp.org/DynaReport/45820.htm

43. F Rayal, Shaping cellular IoT connectivity: emerging technologies inwide-area Connectivity (2015). http://www.xonapartners.com/wp-content/uploads/2015/07/Shaping-Cellular-IoT-Connectivity.pdf.Accessed 1 Dec 2015

44. A Dvir, AV Vasilakos, Backpressure-based routing protocol for dtns. ACMSIGCOMM Comput. Commun. Rev. 41(4), 405–406 (2011)

45. P Li, S Guo, S Yu, AV Vasilakos, Reliable multicast with pipelined networkcoding using opportunistic feeding and routing. IEEE Trans. ParallelDistrib. Syst. 25(12), 3264–3273 (2014)

46. M Khoshkholgh, Y Zhang, K Shin, V Leung, S Gjessing, inWirelessCommunications and Networking Conference (WCNC), 2015 IEEE. Modelingand characterization of transmission energy consumption inmachine-to-machine networks (IEEE, 2015), pp. 2073–2078

47. HS Dhillon, HC Huang, et al., Power-efficient system design forcellular-based machine-to-machine communications. Wirel. Commun.IEEE Trans. 12(11), 5740–5753 (2013)

48. Q Song, L Nuaymi, X Lagrange, inWireless Communications andNetworking ConferenceWorkshops (WCNCW), 2016 IEEE. Evaluation ofmultiple access strategies with power control error and variable packetlength in m2m (IEEE, 2016)

49. A Bartoli, M Dohler, J Hernández-Serrano, A Kountouris, D Barthel, inNETWORKING 2011Workshops. Low-power low-rate goes long-range: thecase for secure and cooperative machine-to-machine communications(Springer, 2011), pp. 219–230

50. S Lien, K Chen, Y Lin, Toward ubiquitous massive accesses in 3GPPmachine-to-machine communications. IEEE Commun. Mag. (2011)

51. XM Zhang, Y Zhang, F Yan, AV Vasilakos, Interference-based topologycontrol algorithm for delay-constrained mobile ad hoc networks. IEEETrans. Mob. Comput. 14(4), 742–754 (2015)

52. Y Niu, Y Li, D Jin, L Su, AV Vasilakos, A survey of millimeter wavecommunications (mmWave) for 5g: opportunities and challenges. Wirel.Netw. 21(8), 2657–2676 (2015)

53. Y Niu, C Gao, Y Li, L Su, D Jin, AV Vasilakos, Exploiting device-to-devicecommunications in joint scheduling of access and backhaul for mmWavesmall cells. IEEE J. Sel. Areas Commun. 33(10), 2052–2069 (2015)

54. T Meng, F Wu, Z Yang, G Chen, AV Vasilakos, Spatial reusability-awarerouting in multi-hop wireless networks. IEEE Trans. Comput. 65(1),244–255 (2016)

55. RY Kim, in Information and Communication Technology Convergence (ICTC),2010 International Conference On. Snoop based group communicationscheme in cellular machine-to-machine communications (IEEE, 2010),pp. 380–381

56. C Tu, C Ho, C Huang, in Vehicular Technology Conference (VTC Fall), 2011IEEE. Energy-efficient algorithms and evaluations for massive accessmanagement in cellular based machine to machine communications(IEEE, 2011), pp. 1–5

57. A Azari, G Miao, in Signal and Information Processing (GlobalSIP), 2014 IEEEGlobal Conference On. Energy efficient MAC for cellular-based M2Mcommunications (IEEE, 2014), pp. 128–132

58. C Pereira, A Aguiar, Towards efficient mobile M2M communications:survey and open challenges. Sensors. 14(10), 19582–19608 (2014)

59. S Plass, M Berioli, R Hermenier, in Future Network &Mobile Summit(FutureNetw), 2012. Concept for an M2M communications infrastructurevia airliners (IEEE, 2012), pp. 1–8

60. Y Chen, Y Yang, in Vehicular Technology Conference Fall (VTC 2009-Fall),2009 IEEE 70th. Cellular based machine to machine communication withun-peer2peer protocol stack (IEEE, 2009), pp. 1–5

61. 3GPP, Evolved Universal Terrestrial Radio Access (EUTRA); User Equipment(UE) procedures in idle mode. TS 36.304 V11.3.0, 3GPP. (2013). http://www.3gpp.org/ftp/Specs/html-info/36304.htm

62. M Gupta, SC Jha, AT Koc, R Vannithamby, Energy impact of emergingmobile internet applications on lte networks: issues and solutions. IEEECommun. Mag. 51(2), 90–97 (2013)

63. T Tirronen, A Larmo, J Sachs, B Lindoff, N Wiberg, in GlobecomWorkshops(GCWkshps), 2012 IEEE. Reducing energy consumption of lte devices formachine-to-machine communication (IEEE, 2012), pp. 1650–1656

64. 3GPP, Study on machine-type communications MTC and other mobiledata applications communications enhancements. TS 23.887 V12.03,3GPP (2013). http://www.3gpp.org/DynaReport/23887.htm

65. SC Jha, AT Koc, et al., in Communications and Networking (BlackSeaCom),2013 First International Black Sea Conference On. Power saving mechanismsfor M2M communication over LTE networks (IEEE, 2013), pp. 102–106

66. H Chao, Y Chen, J Wu, in GLOBECOMWorkshops. Power saving for machineto machine communications in cellular networks, (2011), pp. 389–393

67. SC Jha, AT Koc, R Vannithamby, in CommunicationsWorkshops (ICC), 2014IEEE International Conference On. Device power saving mechanisms forlow cost MTC over LTE networks (IEEE, 2014), pp. 412–417

68. AG Gotsis, NT Koutsokeras, P Constantinou, in Vehicular TechnologyConference, 2007. VTC-2007 Fall. 2007 IEEE 66th. Radio resource allocationand packet scheduling strategies for single-cell ofdma packet networks(IEEE, 2007), pp. 1847–1851

69. D López-Pérez, X Chu, AV Vasilakos, H Claussen, On distributed andcoordinated resource allocation for interference mitigation inself-organizing lte networks. IEEE/ACM Trans. Networking (TON). 21(4),1145–1158 (2013)

70. M Ding, D López-Pérez, R Xue, AV Vasilakos, W Chen, in Communications(ICC), 2014 IEEE International Conference On. Small cell dynamic TDDtransmissions in heterogeneous networks (IEEE, 2014), pp. 4881–4887

71. A Aijaz, AH Aghvami, in Vehicular Technology Conference (VTC Spring), 2013IEEE 77th. On radio resource allocation in LTE networks withmachine-to-machine communications (IEEE, 2013), pp. 1–5

72. A Aijaz, M Tshangini, MR Nakhai, et al., Energy-efficient uplink resourceallocation in LTE networks with M2M/H2H co-existence under statisticalQoS guarantees. Commun., IEEE Trans. 62(7), 2353–2365 (2014)

73. Y Zhang, Tree-based resource allocation for periodic cellular M2Mcommunications. Wirel. Commun. Lett., IEEE. 3(6), 621–624 (2014)

74. GC Madueno, C Stefanovic, P Popovski, Reliable reporting for massiveM2M communications with periodic resource pooling. Wirel. Commun.Lett., IEEE. 3(4), 429–432 (2014)

75. Q Song, X Lagrange, L Nuaymi, in Vehicular Technology Conference (VTCFall). An efficient M2M-oriented network-integrated multiple-periodpolling service in LTE network (IEEE, 2015), pp. 1–6

76. AG Gotsis, AS Lioumpas, A Alexiou, in GlobecomWorkshops (GCWkshps),2012 IEEE. Evolution of packet scheduling for machine-typecommunications over LTE: algorithmic design and performance analysis(IEEE, 2012), pp. 1620–1625

77. AS Lioumpas, A Alexiou, in GLOBECOMWorkshops (GCWkshps), 2011 IEEE.Uplink scheduling for machine-to-machine communications inLTE-based cellular systems (IEEE, 2011), pp. 353–357

78. K Ko, M Kim, et al., A novel random access for fixed-locationmachine-to-machine communications in OFDMA based systems. IEEECommun. Lett. 9, 1428–1431 (2012)

79. DT Wiriaatmadja, KW Choi, Hybrid random access and data transmissionprotocol for machine-to-machine communications in cellular networks.IEEE Trans. Wirel. Commun. 14(1), 33–46 (2015)

80. B Yang, G Zhu, W Wu, Y Gao, M2M access performance in LTE-A system.Trans. Emerg. Telecommun. Technol. 25(1), 3–10 (2014)

81. M Hasan, E Hossain, D Niyato, Random access for machine-to-machinecommunication in LTE-advanced networks: issues and approaches. IEEECommun. Mag. 51(6), 86–93 (2013)

82. W Xu, G Campbell, in Communications, 1992. ICC’92, Conference record,SUPERCOMM/ICC’92, Discovering a NewWorld of Communications., IEEE


http://ieee802.org/16/m2m/#10_0004

http://networks.nokia.com/sites/default/files/document/nokia_lte-m_-_optimizing_lte_for_the_internet_of_things_white_paper.pdf





http://www.xonapartners.com/wp-content/uploads/2015/07/Shaping-Cellular-I oT-Connectivity.pdf

http://www.xonapartners.com/wp-content/uploads/2015/07/Shaping-Cellular-I oT-Connectivity.pdf

http://www.3gpp.org/ftp/Specs/html-info/36304.htm

http://www.3gpp.org/ftp/Specs/html-info/36304.htm



International Conference on. A near perfect stable random access protocolfor a broadcast channel (IEEE, 1992), pp. 370–374

83. CS Bontu, S Periyalwar, M Pecen, Wireless wide-area networks for Internetof Things: an air interface protocol for IOT and a simultaneous accesschannel for uplink IOT communication. IEEE Veh. Technol. Mag. 9(1),54–63 (2014)

84. Cisco, Global Mobile Data Traffic Forecast Update, 2014-2019. Whitepaper. Cisco Visual Networking Index (2015)

85. oneM2M, Functional Architecture. Technical Report TS 0001, V.1.6.1.(2015). http://www.onem2m.org/images/files/deliverables/TS-0001-Functional_Architecture-V1_6_1.pdf

86. R Ratasuk, J Tan, A Ghosh, in Vehicular Technology Conference (VTC Spring),2012 IEEE 75th. Coverage and capacity analysis for machine typecommunications in LTE (IEEE, 2012), pp. 1–5

87. 3GPP, Smart grid traffic behaviour discussion. TSG-RAN R2-102340 (2012)88. Health Informatics - PoC Medical Device Communication - part 00101:

Guide—guidelines for the use of RF wireless technology (2008)89. R Ratasuk, A Prasad, Z Li, A Ghosh, M Uusitalo, in Intelligence in Next

Generation Networks (ICIN), 2015 18th International Conference On. Recentadvancements in M2M communications in 4G networks and evolutiontowards 5G (IEEE, 2015), pp. 52–57

90. D Boswarthick, O Hersent, O Elloumi,M2MCommunications : a SystemsApproach. (Wiley-Blackwell, Oxford, 2012)

91. J Chou, Machine-to-machine (M2M) communications using shortmessage services (SMS). Google Patents. EP Patent App. EP20,110,869,333(2014). http://www.google.com/patents/EP2732565A1?cl=en

92. B Moyer, Low Power, Wide Area: A Survey of Longer-Range IoT WirelessProtocols (2015). http://www.eejournal.com/archives/articles/20150907-lpwa. Accessed 13 Oct 2015

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http://www.onem2m.org/images/files/deliverables/TS-0001-Functional_Architecture-V1_6_1.pdf

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